Adversarial network for natural language synthesis

The key issue with generative task is about deciding what a good cost function should be? GAN(Generative Adversarial Networks) introduces two networks to solve that. The generator network creates fake samples, and Discriminator network distinguishes them from real samples.

GAN has been predominantly applied in image augmentation. GAN is particularly good at generating continuous samples. Due to this reason, it can’t be used directly for text generation (as it's sequence of discrete numbers.).

This talk will cover the recent breakthroughs in applying adversarial networks for language generation.